INOM
EXAMENSARBETE TEKNIK, GRUNDNIVÅ, 15 HP
,
STOCKHOLM SVERIGE 2019
INOM
EXAMENSARBETE TEKNIK, GRUNDNIVÅ, 15 HP
,
STOCKHOLM SVERIGE 2019
Towards robust cross-subject
classification of
electroencephalogram (EEG)
patterns for brain-computer
interfacing (BCI):A feasibility study
SHUAI WU
Abstract
A brain-computer interface (BCI) is a system that enables the subject to send commands with merely brain activity. Such interface is important for people affected by multiple motor disabilities, where BCI made it possible for machine to better understand the patient and thus fulfill their demands.
The BCI variante that base on motor imagery require classification on subject’s brain activity on imagining movement of body parts, which could be done by using different classifier. There exists multiple difficulty when developing such an system, one of them is generalization of trained models, this accuracy of trained model could not be guaranteed when using on a different subject or in a different session. Even within the same session, the classification result is not optimal due to brain activity’s non-stationary nature. This paper tackle the problem of intersubject classification with adaptive importance weighted linear discriminant analysis(AIWLDA), which shows promising result on both intersession and intra-session classification of offline EEG based BCI. This research has shown that there exist subject pairs with inter-subject generalizable potential, more pairs could be revealed by using AIWLDA, but this method fail to robustly classify across every subject-pairs.
Keywords
Sammanfattning
Brain-computer interface(BCI) är ett system där man kan skicka kommandon till dator med bara hjärnaktivitet. En sådan system är viktigt för människor lider av flera motorisk funktionshinder, då maskinen skulle kunna förbättra patienters liv genom att uppfylla deras behov.
Denna rapport fokusera på en variant av BCI, kallas motor imagery based BCI, vilken basera på att klassificera försökspersons hjärnaktivitet då han/hon tänka sig att röra sin kroppsdelar. Det finns flera svårighet för att bygga en fungerande system, en av de är generalisering av tränad model. En tränad model garanti inte exakthet på annat försöksperson eller annat session. Även i samma session, kan model ger sämre resultat på grund av hjärnaktiviteten nonstationary natur. Denna rapport försöka hantera inter-subject klassificering problem med adaptive importance weighted linear discriminant analysis(AIWLDA), som gav bra resultat i både intra-session och inter-session klassificering av offline EEG baserad BCI. Det kommer visa i resultat att det finns försökspersons par där inter-subject generalisering är möjligt och AIWLDA kan avslöja mer av sådana par, men misslyckas att bevisa om det denna egenskap finns mellan alla försöksperson.
Nyckelord
Contents
1 Introduction 1 1.1 Background introduction . . . 1 1.2 Aim . . . 2 1.3 Problem formulation . . . 2 1.4 Delimitations . . . 3 1.5 Outline . . . 4 2 Background 5 2.1 Electroencephalography(EEG) . . . 5 2.2 Brain-computer interface(BCI) . . . 5 2.3 Previous works . . . 6 2.4 LDA . . . 72.5 Covariate-shift adaptation of LDA . . . 7
1
Introduction
This section provide an overview within the topic of brain computer interface and formulate the research question.
Abbreviations
· BCI: Brain-computer interface · EEG: Electroencephalography · LDA: Linear discriminant analysis
· AIWLDA: Adaptive importance weighted LDA · BP: Band Power
· MI: Motor imagery
1.1
Background introduction
Brain-computer interface, allows the user to communicate with machines, providing a new way of communication and control (Wolpaw & Mcfarland 1994). This new channel of control could serve multiple purposes e.g. post-stroke rehabilitation (Prasad & Herman & Coyle & Mcdonough & Crosbie 2009), creates new means of communication for people who suffer from motor disabilities(Hoffmann & Vesin & Ebrahimi & Diserens 2008) or even controlling video games (Van de Laar & Gürkök & Plass-Oude Bos & Poel & Nijholt 2013).
calibrates its model to match the subject’s brain activity pattern(Lotte et al. 2007).
1.2
Aim
Although many of the classification algorithms works fine as it is within the same BCI session for the same subject, the statistical distribution of the data collected from BCI varies across both session and subjects, which limits the transferability of the training data and the trained model across subjects and session (Jayaram et al. 2015). This inconsistency makes each previous model unusable once a new session is started or a new subject is introduced, which results in slow calibration time prior to each session. There have been many studies with the means of decreasing number of calibration trials needed, which, aside from proposing better classifiers, could be summarized by two types of approaches: The first approach is to better utilize calibration data, by either extracting better features (Wang & Gao, S & Gao, X 2005; Boostani & Moradi 2005) or better utilization of these features (Li & Guan & Zhang & Ang & 2014; Sugiyama et al. 1996). The second approach is to make use of existing data, extracting generalized features from earlier data obtained in other sessions or even other subjects (Bolagh & Shamsollahi & Jutten & Congedo 2016; Shenoy & Miller & Ojemann & Rao 2008).
Although generalized features exist in other subjects, it is not guaranteed that these features from every subject will give positive contribution (Shenoy & Miller & Ojemann & Rao 2008). The aim of this study is therefore to find an algorithm that robustly classifies across every subject, which will result in fast or no calibration time for each new installation, making BCI open to everyone in need.
1.3
Problem formulation
nature of brain activities. (Klonowski 2009). Covariate shift refers to the change in the distribution of the input variable in the training and testing sample. Classifier adaptive importance weighted linear discriminant analysis, also known as AIWLDA proposed by Sugiyama et al(2007), could robustly classify samples from a different probability distribution than the test samples.
Non-stationary nature of brain activity is the cause of covariate shift when doing intra-subject classification, non-stationary meaning probability distribution a sample change with time (Klonowski 2009). This nature is not the primary concern of cross-subject classification, where the samples were collected from two or more different sample set, i.e. subjects. Although covariate shift could still be a major problem on the topic since the subjects share similar physiologic structure i.e. are all humans. The research question can, therefore, formulate as such: Is it possible to classify MI tasks robustly across every subject pairs by using covariate-shift adaptation of classifiers? This will help us find out whether the difficulty of intersubject classification lies on covariate shift, thus showing more insight on the topic.
1.4
Delimitations
Due to the limitation of time, the scope of this study is limited to examine a single linear classifier, i.e. LDA. Although many classifiers does have better accuracy compared with LDA (Lotte et al. 2007) due to brain activity’s non-linear nature (Klonowski 2009), LDA is easy to implement and does show a reasonable high accuracy on classifying task of discriminating between left- and right- hand motion imagination (Boostani & Moradi 2005), therefore is chosen to be studied in this project.
Subject-specific frequencies bands are not investigated in this study, which is rather important for motor imagery (MI) BCI. Investigate it will increase the performance of the classification task (Suk & Lee 2011)
1.5
Outline
2
Background
In this section, the reader will get to understand multiple terms on the topic. The method used in the following chapter is also be presented.
2.1
Electroencephalography(EEG)
Electroencephalography (EEG), is a monitoring method to record the electrical activity of the brain, it usually gathers data from electrodes placed on the subject’s scalps (Wolpaw & Mcfarland 1994). These EEG signals collected on the human scalp are a reflection of corresponding activities in upper layers of the brain cortex below the scalp surface (Vidal 1973). Much research has indicated that human has the ability to manipulate a variety of EEG phenomena, which implies multiple possibilities for EEG based BCI (Travis & Kondo & Knott 1975; Mcfarland & A. Miner & Vaughan & Wolpaw 2000).
2.2
Brain-computer interface(BCI)
As stated in the introduction section, BCI enables the user to interact with machines using brain activities, this could be done by letting the system identify patterns of brain activity relevant to commands, which is a task usually given to the classifiers.
The performance of the system depends on both features extracted from the EEG signal and the classifier implemented(Lotte et al 2007). Where features are data extracted from original data by reducing irrelevant parts, it’s intended to be informative and in some cases lead to better human interpretations(Wikipedia 2019b).
This project is built on so-called motor imagery (MI) BCI, where the subject is requested to imagine moving specific body parts when instructed, while electrodes placed along the subject’s scalp record the EEG signal. each of such instruction is called a trial and each electrode is called a channel, with specific names depending on where it’s located (figure 2.1).
Figure 2.1: Electrode placement international 10-20 system
2.3
Previous works
There have been numerous attempts on the cross-subject classification of EEG based BCI, these attempts mainly try to adapt the existing model to decrease calibration time. For instance, a study done by Lu and Zhang (2009) has shown that for P300 speller, a BCI based on decision making, utilizing a so-called subject independent model learned by offline samples could drastically decrease the number of calibration trials needed. Similar studies concerning MI, also shown a positive result(Reuderink & Farquhar & Poel & Nijholt 2011; Jayaram & Alamgir & Altun & Schölkopf & Grosse-Wentrup 2015).
only subjects with positive contribution are used as the training set(Bolagh & Shamsollahi & Jutten & Congedo 2016), whereas others report large variance on accuracy between different subjects, when using inter-subject generalized features(Shenoy & Miller & Ojemann & Rao 2008)
2.4
LDA
Earlier studies have shown that Linear discriminant analysis(LDA), a linear classifier, which discriminates between two classes, is reasonably accurate on classifying MI tasks (Herman, 2015). The classifier tries to find a line, where the labeled samples are separated by the origin when projected on the line. The unlabeled samples could then be projected on the line, and each observation are labeled depending on the position of the projection compare with originn.
When classifying a set of offline samples using 4-fold cross-validation, LDA shows similar average accuracy for intra-session(62.7%) and inter-session classification (60.2%), but is lacking in an inter-subject(<50%) classification overall, except for some specific subject pairs. Although the accuracy is not as high as the study done by Herman et al. (2015) due to no subject-specific parameters was investigated in this study, but the result can still serve the purpose of comparison with the covariate-shift adaptation of LDA.
2.5
Covariate-shift adaptation of LDA
Assume the ratio of the test and training probability density function is finite and known:
P1(x) P2(x)
covariate-shift refer to the effect, where the change in probability distribution presents in the training and test data. AIWLDA is a modified LDA classifier that taken in to account of this by utilizing the importance, making this classifier more accurate when encounter covariate-shift.
AIWLDA has a model of:
f (x, θ) = θ0+∑ i
θixi
Where θ is learned as following:
θ = argminθ[ n ∑ i=1 1 n(( Ptest(xi) Ptrain(xi) )λ(f (xi, θ)− yi)2]
The classifying result, or the labels are then obtained by:
u = sgn(f (x, θ))
Here each xiand yipairs denotes one labeled observation, where xiis the input and yi indicates its label. The importance is between the testing and training input’s probability density function, which the input in importance expression is just the training input of each observation.
Note that λ is the parameter that controls the tradeoff between accuracy and precision(Sugiyama, Krauledat & Muller 2007), known as the bias-variance tradeoff(Lotte et al 2007). Model selection is needed to choose a suitable λ. Worth noting is that when λ = 0, AIWLDA is no other than the normal LDA.
3
Method
This section will provide more insight on both data and method used in this study, an evaluation on measures of results are also be presented.
3.1
Data
In this project, the subject’s EEG pattern is recorded by 2 electrodes, placed on the C3, C4 channels according to the international 10-20 system (Figure 2.1), which corresponding to the hand area in M1 (Wang & Gao & Hong & Gao 2010). For every four seconds, the subjects are instructed to imagine moving either right or left hand, each of such an instruction is called a trial and each session consists around 140 trials.
3.1.1 Preprocessing
This study make use of the feature called band power(BP). In order to obtain this feature, raw EEG signal recorded from each session needed to be processed, extracting band power from it with respect to frequencies. This could be achieved by using Fourier transformation on different time intervals that are reasonably small, in this study, the time interval is chosen to be ¼ of a second. This creates one 3d-array of trial × Time × Frequency for each channel respectively, where frequency spans from 0 to 41 Hz. Each element in the observation indicates the BP of the particular frequency in the time interval of that trial. This process is called preprocessing.
3.1.2 Feature extraction
Earlier studies have shown the correlation between motor imagery of left and right hand and activities in Mu- and Beta-rhythm. By performing MI tasks, the subject can learn to control band powers (BP) in respective rhythms band(Mcfarland & A. Miner & Vaughan & Wolpaw 2000). While the opposite can also occur, i.e. classifier could use BP from respective bands to obtain enough information to determine whether the subject imagine moving right or left hand(Pfurtscheller & Neuper, & Flotzinger & Pregenzer 1997). In order to extract features in Mu- and Beta- rhythm. BP needs to be averaged over the respective rhythm band, reducing each observation to 4 elements(2 rhythm for each electrode).
Furthermore, Since EEG signals might not generalize well across the whole trial duration, a selection is then performed for every trail in each session, on time windows of 1 second with 0.25 seconds overlap, obtaining 7 different time windows for each session. These windows, which, contain 4 different observations for each trail, are viewed as subsets of observations with the same probability distribution.
A method called cross-validation is introduced here. The basic idea is to divide a set of samples into training sets and validation sets, the risk is then estimated by the performance of the validation. A commonly used cross-validation type called k-fold cross validation is applied here, which divides the sample set into k equal-sized subsets. Using one subset at a time as a validation set and every other k-1 subsets as training sets, this process is repeated k times, until all the subset had been validated once. The accuracy is estimated by the mean accuracy of all the validation.
like voting to increase accuracy of classification, but in this case where test sample size is small, the extra observations are used as it is, so the accuracy could be better discriminated with the binomial distribution of p = 0.5, which have a decreasing variance when the sample size increases.
3.1.3 Parameter selection
Recall that the term consisting importance in AIWLDA requires the probability density function of test and training input. It is assumed that the Mu- and Beta-frequency follows a multidimensional normal distribution(Sugiyama & Krauledat & Muller 2007), with probability density function:
P (x) = exp(− 1 2(x− µ) TΣ−1(x− µ)) √ det(Σ)(2π)k . Here Σ denotes covariance matrix, calculated by:
Σij = E(xi−µj)(µi−xj)
µdenotes the mean vector and k the sample size.
Both the covariance matrix and the mean vector could be calculated using input from testing and training samples respectively, thus obtaining the importance.
Parameter λ that controls the bias-variance tradeoff in AIWLDA is a parameter dependent on both training and testing samples. Different λ indicates a different model, therefore, a new optimized λ must be decided for each new learning-testing subject pair. This is done by model selection using 4-fold cross-validation on each subject pair with λ chosen from{0.1, 0.2…1.0}.
3.2
Accuracy measurement
sample.
Two sort of classification are performed in this project, namely intra-subject and cross-subject classification.
Intra-subject classification is performed on all 10 sessions, where the accuracy is obtained by 4-fold cross validation.
The session with best intra-subject result from each subject are used in cross-subject classification, by using one of the five sessions as training set, another as testing set. This gives in total 20 different training-testing pairs, where the whole training session are used for training and the accuracy is obtained by classifying testing session with trained model. Note that cross-validation could not be performed here since testing and training samples are from different populations.
Accuracy within 95% confidence interval of binomial distribution of p=0.5 is deemed to be not significant enough. With a sample size of 560, this corresponds to 46% - 54%, result outside this interval are referred as valid results, where pair with lower than 46% accuracy signify not cross-subject classifiable and pairs with higher than 54% are cross-subject classifiable.
3.3
Sub-problems
The research question: ’Is it possible to classify MI tasks robustly across every subject pairs by using covariate-shift adaptation of classifiers?’ could be divided into three minor parts. In this section, we shell first formulate these sub-problems, then showing how they are solved.
3.3.1 Formulation
Secondly, we need to show that all of the pairs are cross-subject classifiable. This part is responsible for generalizability, i.e. whether this method could extend to every subject pairs.
lastly, determine whether the accuracy of cross-subject classification have a comparable magnitude to intra-subject classification when using AIWLDA. This part answers for the robustness of the covariate-shift adaptation. Since the accuracy of cross-subject classification is limited to be lower than or equal to intra-subject classification. If covariate shift is the underlying problem on cross-intra-subject classification, solving it indicate cross-subject classification have similar accuracy as intra-subject classification.
If all three parts could be validated and show a positive result, we can conclude that covariate shift adaptation of classifiers can classify MI tasks robustly across every subject pairs.
3.3.2 Measure evaluation
The first part is solved first by determining if result of cross-subject classification obtained by two classifiers are from the same probability, using Mann–Whitney U test (Nachar 2008), then show that AIWLDA have a higher average accuracy than LDA on cross-subject classification.
The second part is then evaluated by examine the result obtained by AIWLDA in cross-subject classification, so that none of the pairs shows < 46% accuracy. Note that if some of the result lies within 46% - 54%, we can neither proof or disprove the research question. In this case, a more accurate classifier and better feature extraction section is required, in order to obtain a valid result.
4
Result
Results are presented in this chapter, with the helps from these results, the research question could then be answered.
4.0.1 Justification
AIWLDA(Table 4.2) shows 9 out of 20 learning-testing pairs with higher than 54% accuracy, which in Section 5.2 described as cross-subject classifiable, compare to LDA (Table 4.1) with only 4 out of 20 pairs. This proves that covariate shift adaptation does outperform the original classifier, which justifies using the covariate shift adaptation on cross-subject MI classifying.
Mann–Whitney U test gives z = -2.62, which signify that result from respective samples are from different distributions. Figure 4.1 further show that AIWLDA outperform LDA in cross-subject MI tasks, by comparing the mean accuracy of 55.3% with LDSs 45.1%.
Worth noting is, none of the pairs from Table 4.2 are lower than 46%, thus neither prove or disprove on generalizability aspect.
Table 4.1: Cross-subject classification with LDA.
Table 4.2: Cross-subject classification with AIWLDA. letter indicates subject, (o) indicates cross-subject classifiable
4.0.2 Robustness
performing U test again on result from intra-subject classification and inter-subject classification with AIWLDA shows they belongs to different distribution, with z = 2.95, showing result from intra- and cross-subject classification are not from the same distribution with confidence level < 99%. After that we compare mean accuracy over them (Figure 4.2), where intra-subject classification with AIWLDA have mean accuracy of 62.4%. This far exceed the 55.3% obtained in cross-subject classification.
This result proves, there exist factors other than covariate shift, which contribute to the inaccuracy of cross-subject classification. This method is proven to be not robust enough to classify an cross-subject MI task.
LDA AIWLDA 0 20 40 60 80 100 45.1 55.3 #Accuracy(%)
Figure 4.1: Cross-subject classification mean accuracy with standard deviation
Intra-sub Cross-sub 0 20 40 60 80 100 62.4 55.3 #Accuracy(%)
5
Discussion
In this section, future implications of result are discussed, along side with the weakness and the strength of the proposed method. At last, we shell discuss the future of BCI.
5.1
Weakness
As described in Chapter 4 (Result), using adaptive importance weighted LDA will increase overall accuracy of cross-subject classification, but the mean accuracy is far from optimal and the variance is large. Beside this, an optimized parameter λ is not easy to find, due to the limitation of computation power. Even when an optimized λ is found for a specific training-testing pair, the model confine this parameter to only work on the specific model. Moreover, due to non-stationary nature of brain-activity, model trained on two specific cross subject sessions is not guaranteed to give good result on other sessions from the same testing subject, or even different samples from the same test session. This variant of covariate shift adaptation is therefore used, more as a verification for the possibility of cross-subject classification, when addressing covariate shift.
5.2
Strength
method presented in this study with these findings might further increase performance of subject independent BCIs based on Motor imagery.
5.3
Future work
As to future studies, one could investigate the subject pairs that are not cross-subject classifiable (<44% accuracy) with LDA, but being the opposite when using AIWLDA(>55% accuracy). An idea is to compare the cross-subject classification accuracy of multiple different classifier with their covariate shift adaptation, this might show more interesting results and a glimpse of the reason behind said problem.
Due to the limitation of the project time, no subject specific parameters e.g. Mu-and Beta- bMu-ands were investigated, which contributes to the low classification accuracy. Having better feature extracted from preprocessed data might raise the accuracy. Since cross-subject classification should work both way and there are many subject-pairs with only one learning-testing pair that is cross-subject classifiable, it is safe to assume that a higher accuracy will most definitely reveals more classifiable pairs. Generalizability could also be determined this way, either all subject pairs being cross-subject classifiable or showing a higher variance that indicates the opposite.
5.4
Outlook
The generalizability of BCI is an important topic, this allow features obtained from one subject to be used on others. Many people who urgently require assistant of BCI are patients suffering motor impairment, these patients might not have sufficient mental and physical strength to undergo the long lasting calibration session. Therefore by constructing a subject independent BCI using data collected from healthy subject, requirements earlier placed on the user could be minimized.
studies could help to identify easily as to which individual belongs to which cross-subject classifiable group, then we could achieve a cross-subject independent BCI that is robust on every new user with minimal calibration. There are already studies with similar problem formulated, e.g. study done by Cantillo-Negrete et al. have taken gender in to account and shown increasing performance on subject independent BCI.
6
Conclusion
Using covariate shift adaptation of LDA, also known as AIWLDA does show better performance on cross-subject classification ,with mean accuracy of 55.4% compared with 45.1% obtained by LDA (Figure 4.1). Overall only some specific pairs contribute to higher cross-subject accuracy when using AIWLDA, not all subject pairs show explicitly being cross-subject classifiable, i.e. accuracy > 54%. The generalizability is therefore still to be determined.
Comparing 55.3% of cross-subject accuracy and 62.4% of intra-subject accuracy (Figure 4.2) and applying U-test between these results shows, that the cross-subject accuracy with AIWLDA is far from optimal, thus conclude that the method is not robust when classifying across all subjects.
In conclusion, the covariate shift adaptation of classifier is not robust across every subject, but does out perform the original classifier in cross-subject classification accuracy. The generalizability across every subject pair is still to be determine, due to the low accuracy achieved in this study.
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